broadhurst thesis
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MODELING ADSORPTION OF CANE SUGAR
SOLUTION COLORANT
IN PACKED-BED ION EXCHANGERS
A Thesis
Submitted to the Graduate Faculty of the
Louisiana State Unversity and Agricultural and Mechanical College
in partial fulfillment of the
requirements for the degree of
Master of Science in Chemical Engineering
in
The Department of Chemical Engineering
by
Hugh Anthony Broadhurst
B.S., University of Natal, 2000
August, 2002
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ACKNOWLEDGEMENTS
The author wishes to thank all of the staff at the Audubon Sugar Institute
that had an input on the project. Particular thanks must be given to Dr P.W.Rein for
his guidance and motivation, Brian White and Lee Madsen for their expertise in the
field of HPLC analysis, and Len Goudeau and Joe Bell for their assistance in the
crystallization test.
Thanks go to the sponsors, Tongaat-Hulett Sugar Limited and Calgon
Carbon Corporation for providing the funds for this research.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS........................................... ii
GLOSSARY OF TERMS.................................................. v
NOMENCLATURE...........................................vii
ABSTRACT....................................................................... ix
CHAPTER1. INTRODUCTION......................................... 1
1.1. The White Sugar Mill Process....................... .... 1
1.2. Research Objectives.......................... .....4
2. BACKGROUND....................................... 6
2.1. Cane Sugar Colorant................................. ..... 62.2. Quantifying Colorant.................................... ..... 8
2.3. Removal of Cane Sugar Colorant..................... . 10
2.4. Color Transfer in Crystallization.......... ......... 17
3. THEORY............................................... 20
3.1. Axially Dispersed Packed-Bed Adsorption Model. 20
3.2. Plug Flow Adsorption Model................... ......... 233.3. Numerical Solution Technique................... ... 28
4. MATERIALS AND METHODS................. ................. 31
4.1. Experiments................................ 314.2. Sample Analysis......................................................................... 38
5. RESULTS AND DISCUSSION...................... ............. 455.1. Color Formation Investigation.................. ..... 45
5.2. Ultrafiltration.......................... 54
5.3. Strong-Acid Cation Resin....................................................... 565.4. Weak-Base Anion Resin............................................................. 66
5.5. Decolorizing Resin............... ................. 71
5.6. Regeneration Aids.......................................... 755.7. Color Transfer in Crystallization............................ 77
6. CONCLUSIONS........................................... 806.1. GPC as an Analytical Tool..................................... 80
6.2. Validity of the Plug-Flow Model............................ 80
6.3. SAC Resin.................. ........... 816.4. WBA Resin................................ .... 82
6.5. Decolorizing Resin......................... ....... 83
6.6. WSM Process Design........................... ..... 836.7. Future Research Directions.................... ........ 84
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REFERENCES.................................................................. 86
APPENDIX
A. SAMPLE CALCULATIONS............ ............. 91
B. SAC RESIN RESULTS.................. ........ 102
C. WBA RESIN RESULTS........................... ..... 120D. DECOLORIZING RESIN RESULTS. 138
E. MATLAB CODE......... 151
VITA...................................................................... 161
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GLOSSARY OF TERMS
Affination The process of removing the molasses film from sugar
crystals with a saturated sugar solution
Ash Inorganic dissolved solids
ABS Absorbance
Breakthrough When the adsorbent can no longer absorb all of a solute
species from the feed.
Brix Total dissolved solids (%m/m)
Chromatography A term for methods of separation based upon the portioning
of a solute species between a stationary phase and a mobile
phase
DECOL Decolorizing resin
GPC Gel Permeation Chromatography
HPLC High performance liquid chromatography
ICUMSA International Commission for Uniform Methods of Sugar
Analysis
MW Molecular weight
Pol Apparent sucrose content (% m/m)
Purity Percent of pol (or true sucrose) to brix
RI Refractive index
SAC Strong-acid cation ion exchange resin
WBA Weak-base anion ion exchange resin
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WSM White Sugar Mill The process of making white sugar
directly from sugarcane using ultrafiltration and ion
exchange.
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NOMENCLATURE
Symbol Description Units
A Column cross-sectional area m2
As Sample absorbance at 420nm AU
C Concentration in bulk fluid mVC
* Concentration of fluid in equilibrium with adsorbent mV
C0 Feed concentration mV
Ci Concentration of component i g/ml or mV
da FEMLAB Time derivative coefficient matrix
dp Particle diameter m
D Axial dispersion coefficient m2/min
DAB Diffusivity of component A in B m2/s
E Activation energy J/mol
F FEMLAB Remaining terms in PDE vector
JD Chilton-Colburn analogy J-factor [-]
k' Effective mass transfer coefficient 1/minkLa Mass transfer coefficient 1/min
kc Mass transfer coefficient in Geankoplis correlation m/s
kr(T) Reaction rate 1/min
k0 Term in Arrhenius expression 1/min
K Adsorption parameter q = K.C* [-]
K(t) Time varying adsorption parameter [-]
KC0 Adsorption parameter based on initial concentration [-]
Keq Equilibrium adsorption parameter [-]
K0, K1 Parameters inK(pH) [-]
L Column length M
MA Molecular weight [-]
n FEMLAB Outward normal on domain boundaryq Concentration on solid phase AU
q0 Initial resin concentration AU
Q Volumetric fluid flow rate m3/min
R FEMLAB Dirichlet boundary condition vector or
Universal Gas Constant
Re Reynolds number Re = dui/ [-]
Sc Schmidt number Sc = /DAB [-]
St Stanton number St = k'L/ui [-]
t Time variable min
t0 Peak time of Gaussian distribution or
Initial time parameter in batch tests
min
T Temperature K
u FEMLAB Dependent variables vector
u0 Superficial fluid velocity m/min
ui Interstitial fluid velocity m/min
Vbed Volume of resin in packed-bed (voidage measurement) ml
Vliquid Volume of liquid (batch tests) ml
Vresin Resin volume measured as a packed-bed in a measuring
cylinder (batch tests)
ml
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ABSTRACT
The removal of cane sugar solution colorant by packed-bed ion exchangers
was modeled using a linear driving force (LDF) adsorption model. Adsorption of
colorant is of interest to the developers of the White Sugar Mill (WSM) process as it
is a complex subject.
The problem is that color is an indiscrete mixture of many components
making it difficult to measure and even more challenging to model. Colorant
formation was investigated using gel permeation chromatography (GPC) with the
objective of developing a method to define pseudo-components representative of
cane sugar solution colorants.
WSM is a process for producing white sugar directly from sugarcane in the
raw sugar mill by using ultrafiltration and continuous ion exchange technology.
The ion exchange resins employed were a strong acid cation (SAC) resin in the
hydrogen form, a weak base anion (WBA) resin in the hydroxide form and a
decolorizing resin in the chloride form. Decolorization using the three resins was
then analyzed using the GPC pseudo-component technique.
Batch testing of the resin allowed the development of equilibrium isotherms
that could be substituted into a standard LDF model. Column testing was then
performed to investigate the dynamics of adsorption of colorant in packed-beds.
Linear isotherms were measured for each of the three resins, indicating that
the colorant is dilute. Results indicated that a plug-flow model with a constant
linear isotherm was sufficient in all cases except the SAC resin. The SAC
adsorption parameter decreased sharply as the pH increased, causing colorant to be
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x
desorbed from the resin. This situation must be avoided if optimal decolorization is
to be achieved.
The adsorption models can be utilized in the design of a WSM process to
optimize the decolorizing capacity of the resins.
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CHAPTER 1. INTRODUCTION
1.1 The White Sugar Mill Process
1.1.1 The Production of White Cane Sugar
The production of white cane sugar is currently a two-step operation. Raw
sugar is light brown in color and is produced in sugar mills. Mills are located close
to the cane growers to minimize cane degradation and transportation costs. The raw
sugar is subsequently transported to a refinery where the remaining impurities are
removed. Figure 1.1 shows the basic steps in the production of raw sugar from
sugarcane. Sucrose is first extracted from sugar cane with water, by counter-current
milling or cane diffusion. The juice is screened, heated to its boiling point, and then
flashed. Suspended solids and colloidal materials are then precipitated with milk of
lime (calcium hydroxide solution) and settled in a clarifier. The resulting clear juice
is evaporated to approximately 65% dissolved solids in a multiple effect evaporator
train. Sugar is then crystallized from the syrup in a three-stage crystallization
process. After each crystallization step, sugar crystals are separated from the
mother liquor in centrifuges. The raw sugar is then transported to the refinery
where it is dissolved, purified and re-crystallized to white sugar.
Cane ExtractionDJ
HeatingMJ
ClarificationCJ
EvaporationSy. 3 Stage
Crystallization
Ma.Centrifugation
RS
Mol
Key:DJ = Draught juice; MJ = Mixed Juice; CJ = Clear Juice; Sy. = Syrup; Ma. = Massecuite
RS = Raw sugar; Mol. = Final Molasses
Figure 1.1: Raw sugar mill flowsheet
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1.1.2 The White Sugar Mill
There are three main areas in which the profitability of the raw sugar mill
may be increased (Fechter et al., 2001):
1. Improve the quality of the sugar produced
2. Increase overall recovery of sugar
3. Make use of products in the molasses
The sugar refinery is a simple and relatively low cost operation except for the
significant costs in transporting raw sugar from the mill and sugar losses in the
refining process. These costs could be removed by producing white sugar at the raw
sugar mill.
Recent advances in membrane and continuous ion exchange technology
have been utilized by Tongaat-Hulett Sugar Limited and S.A. Bioproducts Limited
in the development of a process to produce white sugar directly in the raw sugar
mill (Rossiter, 2002). The process design may be incorporated into an existing raw
sugar mill (see Figure 1.2).
Cane ExtractionJuice
HeatingClarification
Evaporation 4 Stage
CrystallizationCentrifugation
White
Sugar
Whitestrap
Molasses
UltrafiltrationRefrigeration
& HX
Cation ISEP
Anion ISEP Decolorization
Figure 1.2: White sugar mill flowsheet
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Juice from the existing first evaporation effect at 20 to 25% brix is first
ultrafiltered. This removes high molecular weight material from the syrup that
would otherwise irreversibly foul the ion exchange resins. The retentate (the
material rejected by the membrane) may be used as a feedstock to a neighboring
distillery or may be recycled to the clarifiers. Impurities leave the system in the
clarifier mud. The permeate from the membrane unit must be refrigerated to 10oC
as in the subsequent ion exchange separations low pH conditions are experienced.
Under acidic conditions, sucrose breaks down to fructose and glucose. This reaction
is termed inversion in the sugar industry.
The heart of the process is the continuous ion-exchange demineralization
using Calgon Carbon Corporations ISEP technology (Fig 1.3). An ISEP is similar
to a conventional Simulated Moving Bed (SMB) that uses switching valves to
achieve a continuous process. The ISEP differs in that it uses a rotating carousel of
packed-beds about a central feed valve that is made up of a stationary and rotating
element. ISEPs have been used in the South African sugar industry at the Tongaat-
Hulett Sugar Refinery to deash high-test molasses (HTM). The inorganic
constituents of sugar solutions are commonly termed ash and so the
demineralization resins have been named deashing resins.
Two demineralization resins, a strong acid cation (SAC) and a weak base
anion (WBA), are used in series to remove inorganic and charged organic impurities
(primarily organic acids). Despite some decolorization, the resulting high purity
juice still has significant color that must be removed in the decolorization ISEP.
The decolorizing resin used is a sugar industry standard, a strong base anion resin in
the chloride form. The decolorized juice produced from the WSM process is of
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such high purity and low color that four crystallization stages maybe performed.
The benefits of the process include (Rossiter, 2002):
i. Increase in yield
ii. Increase in sugar quality: white sugar not raw sugar is produced
iii. Production of high-grade molasses (termed whitestrap molasses)
iv. No fouling in evaporators and vacuum pans
v. Higher heat transfer coefficients in pans and evaporators
Figure 1.3: A pilot scale ISEP
1.2 Research Objectives
Ion exchange demineralization has been shown to remove 95% of the ash
content of the ultrafiltered syrup (Fechter, 2001). In parallel with the ash removal,
is an 80% reduction in color. It is of significant interest to the process developers to
investigate the removal of color by ion exchange resins. If the color adsorption
could be modeled then the process design could be optimized to make best use of
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the resins. This may reduce the currently high loading on the decolorizing
operation.
There is currently no complete model of cane sugar colorant (Godshall &
Baunsgaard, 2000). The sugar industry standard color measurement groups all
colored bodies as one component. This is a major assumption. For modeling
purposes, it would be useful to define pseudo-components that represent cane sugar
colorant. An investigation into cane sugar color formation will give valuable
insights on how to define these components.
Interaction between components could be assumed negligible since colorants
are so dilute. This would allow the use of a number of single component models to
represent adsorption of color onto the resins. The specific goals in the research are:
Develop an analysis technique to measure color
Use this analysis to investigate color formation
Apply results from the color formation trials to define pseudo-
components to be used in modeling
Perform batch adsorption tests to investigate the resin equilibrium
properties
Develop a packed-bed adsorption model using the equilibrium
properties
Perform column loading experiments and regress model parameters
for each resin
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CHAPTER 2. BACKGROUND
2.1 Cane Sugar Colorant
2.1.1 Color in the Sugar Industry
The goal of any production process is to produce as large a quantity of
product within the quality criteria. One of the most important criteria in the sugar
industry is the color of both raw and white sugar. Consumers and other users (e.g.
carbonated beverage manufacturers) of white sugar expect a white product. Raw
sugar (light brown in color) produced in the mills is also subjected to a quality
standard. Higher color raw sugar requires more effort on behalf of the refiner to
produce a white product.
2.1.2 Types of Colorant
Sugar colorant is unfortunately not one single molecular species. It consists
of a wide range of materials each with its own molecular weight (MW), pH
sensitivity, charge, and chemical structure (Godshall & Baunsgaard, 2000).
Research into the complex organic nature of cane sugar colorants has been a major
area of interest in the sugar industry since its beginning. Understanding more about
the character of color allows for fine-tuning existing separation processes and for
designing new and better techniques for its removal.
Colorants are often named from their origin and mechanisms of formation
(Godshall et al, 1988). Caramelization and alkaline degradation are similar thermal
mechanisms except that alkaline degradation occurs at high pH and forms much
darker colorant (Godshall, 2000). The Maillard reactions occur throughout the
factory and have many complex pathways (Van der Poel et al, 1998). They proceed
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under almost all conditions, as reducing sugars and amines or amino acids are
always present except in the purest of solutions. Iron also plays an important role,
particularly in plant-derived colorants (Godshall, 1996). Many polyphenolic
compounds found in cane juice are able to produce highly colored iron complexes.
It must be noted that just as important as the colorants themselves are the
compounds that are color precursors. These, often colorless, compounds can react
to form highly colored species. Table 2.1 summarizes the general types of colorant
found in a cane sugar mill (adapted from Godshall, 2000). Cane sugar colorant is a
difficult issue as it is so difficult to define.
Table 2.1: Types of sugar colorants
Colorant Type General Characteristics
Phenolic
Low MW colorless to light yellow precursors; darken at high pH;
oxidize to form yellow and brown polymers; react with polyphenol
oxidase to form light yellow to dark brown colorants. Darken in
presence of iron.
Caramel
The result of thermal degradation of sucrose; low net charge; wide
color range from yellow to brown; MW 500 to about 1,000; MW and
color increases as thermal destruction proceeds.
Alkaline Degradation
Products (ADPs)
Similar to caramels, but much darker in color; form at high pH.
Melanoidin
Maillard reaction reaction products of amino acids with reducing
sugars; reaction occurs rapidly at alkaline pH; products are dark brown.
Colorant Polysaccharide
Complex
Polysaccharides formed in cane have phenolic groups and dicarboxylic
acid functionalized lipids that can bind with colorant to make a very
high MW product. Occludes preferentially into the crystal.
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2.2 Quantifying Colorant
2.2.1 ICUMSA Color
The industry standard sugar solution color measurement is the International
Commission for Uniform Methods of Sugar Analysis (ICUMSA) color method. A
sugar juice free of suspended solids, corrected to pH 7, and of known solids
concentration is analyzed using a spectrophotometer set to 420nm (SASTA1
laboratory manual). The color is calculated as follows:
bc
AS
000,10color420ICUMSA
= (2.1)
The absorbance, , is divided by the product of the dissolved concentration, c
(g/ml), and the cell width, b(mm).
SA
ICUMSA 420 color is a measurement to give an indication of the overall
color of the juice. This is useful in evaluating the color removal performance of a
process. Clearly, no information is given about the specific types of colorants
present in the sample. Knowing the types of colorant is useful, for example, if a
syrup has a high concentration of a substance with no affinity for the sugar crystal it
will be of high ICUMSA color. According to the ICUMSA color the syrup would
produce a high color product but in practice it would not. Similarly, low ICUMSA
color mother liquor can produce sugar of higher color than would normally be
expected.
2.2.2 Gel Permeation Chromatography
Gel permeation chromatography (GPC) is a liquid chromatography method
that separates a sample based on molecular size.A small sample is injected into a
1South African Sugar Technologists Association
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stream of a buffer solution that flows into a column packed with a gel of precisely
controlled pore size. The gel pores are arranged in such a size distribution that
small molecules are able to diffuse into the pores whereas larger molecules are
excluded. A detector is used at the end of the column to measure the concentration
of the material exiting the column. Typically, a refractive-index (RI) or an
ultraviolet-visible (UV-VIS) detector is used.
The analysis may be calibrated by injecting standards of precise molecular
weight into the column. If the samples to be analyzed are of the same molecular
size and shape as the standards, their weights may be read off the calibration curve.
The buffer solution masks the gel from any ionic behavior of the sample, as no
interaction is wanted between the analyte and the stationary phase.
Many authors have made use of GPC to analyze sugar solutions, including
Shore et al (1984), Godshall et al (1988, 1992a, 2000), Bento et al (1997) and Saska
& Oubrahim (1987). Of particular interest is the work of Godshall (1992a). The
removal of high molecular weight colorants in batch experiments was measured
using GPC. The resulting chromatograms all had three distinct peaks. Each peak
was treated as a single pseudo-component to investigate the decolorizing ability of a
number of different adsorbents. Saska & Oubrahim (1987) report that GPC is a
reliable method to investigate the molecular weight effects of decolorization
mechanisms. The WSM process has been investigated using this principle except
that it was applied to the dynamics of the process and not just the overall
decolorization.
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important, as the membrane must be able to withstand the harsh cleaning chemicals
used to remove buildup of foulants in the pores.
In many industrial separation processes membrane filtration has been used
effectively. This unit operation has, however, only been incorporated into one sugar
production facility (in Hawaii, using the New Applexion Process), despite
considerable interest by many researchers (Steindl, 2001). The Sugar Research
Institute in Australia has been researching ultrafiltration since 1975. Membrane
filtration can drastically increase sugar quality, and give rise to higher crystal
growth rates (Crees, 1986) but it was concluded that capital and operating costs
were excessive.
Suspended solids, colloidal particles and soluble high molecular weight
material can be removed using membrane filtration. Average performance data
(Steindl, 2001) show the effectiveness of this unit operation in removing impurities
from clarified juice:
Purity rise 0.45 units
Removal of
Turbidity 95%
Dextran 98%
Starch 70%
Total polysaccharides 80%
ICUMSA Color 25%
Membrane suppliers offer a wide range of pore sizes, however no major
difference in color removal is experienced (Crees, 1986; Kochergin, 1997; Patel,
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1991) unless the pore size is reduced to below 20,000MW. Use of the lowest
MWCO is not practical as the sugar produced is not of significantly less color than
of sugar produced from membranes of higher MWCO (Cartier et al, 1997).
Membranes may be sized primarily on minimizing the membrane area (capital cost)
and maximizing the permeate flux (Fechter et al, 2001).
One of the problems associated with membrane separation is that the
retentate stream contains sucrose. It is not economic to simply dispose of this
stream and so a number of researchers have proposed methods to recycle the
retentate or use it for some other purpose. Proposals include:
Dilution of the retentate stream followed by a secondary filtration
(Steindl, 2001)
Clarification of the retentate using a flotation clarifier (Steindl,
2001)
Recycling the retentate to the existing settling clarifiers (Rossiter
et al, 2002)
Using the retentate as a feed to an attached ethanol facility
(Rossiter et al, 2002)
Membrane technology may be applied to raw cane sugar mills after the lime
defecation and clarification stage. Steindl (2001) reports that raw juice clarification
removes the insoluble solids and some soluble material. The lower impurity
concentration found in clarified juice allows higher filtration fluxes and reduces the
risk of erosion on the membrane surface.
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Urquhart et al, (2000) report that filtering clarified juice with a membrane
unit allows the production of high pol, low color sugar to satisfy the Australian QHP
(Queensland High Pol) standard. Another installation allowed the production of a
super VLC (very low color) sugar (Kwok, 1996). High quality sugar produced
using this technique allowed the Crockett refinery in California to eliminate both the
affination and the remelt stations. Balakrishnan et al (2000) investigated the use of
ultrafiltration to produce a plantation white sugar with a color of approximately 150
ICUMSA units.
Ultrafiltration has also been suggested as a pretreatment since it generally
cannot produce a syrup of high enough quality to directly crystallize white sugar
(Steindl, 2001). Ion exchange and chromatography require a very clean feed, to
protect the resin from fouling. Membrane filtration has proved to be a very
effective pretreatment (Fechter et al, 2001), allowing the use of a single set of resin
for a period longer than the length of an average South African season (about 9
months). Saska et al (1995) proposed the use of nanofiltration following
ultrafiltration to produce an upgraded syrup from which white sugar could be
crystallized. Monclin and Willett (1996) proposed using adsorptive decolorization
of ultrafiltered juice. Amalgamated Research Inc. has developed and patented a
direct white sugar production process using ultrafiltration followed with
chromatography (Kearny, 1999a). Lancrenon et al (1998) propose the use of
microfiltration in the sugar refining process.
Despite the numerous investigations into membrane separations in the cane
sugar industry there has been no widespread adoption of the unit operation (Steindl,
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2001). It is likely that the next major installation of a membrane unit will be as a
pretreatment to either ion exchange or chromatography. The use of ultrafiltration in
the sugar industry is limited by economics. This unit operation will not make an
appearance in the sugar industry until a process with proven economics is
developed. It is likely that ultrafiltration will be used in series with another
separation process.
2.3.2 Decolorization with Ion Exchange Resins
Since the 1970s, with the advent of macroporous strong-base anion ion
exchange resins, ion exchange resins in the chloride form have become the sugar
refinery workhorse decolorizer. Despite increased effluent disposal problems, the
lower capital and operating costs of fixed-bed ion-exchangers have caused them to
replace activated carbon and bone char decolorization (Van der Poel et al, 1998).
Factors affecting the ion exchange process are:
Color to ash ratio
Color content
Type of colorant
Impurity concentration (viscosity)
Sugar colorants are fixed to strong-base anion exchange resins by ionic
bonding and/or by hydrophobic interactions (Bento et al, 1996). Bento (1996)
investigated the removal of colorants by Rohm & Haas Amberlite 900 resin:
Caramels 62.8%
Melanoidins 97.5%
Alkaline degradation Products 98.0%
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Caramels are least retained by the resin, as they are relatively uncharged whereas
the other colorants are anionic in alkaline medium.
Morley (1988) made a detailed study of fixed-bed decolorizing ion
exchangers. Color was measured by the ICUMSA color method. An analytic
mathematical model was derived assuming no axial dispersion and constant linear
isotherms. Model parameters were estimated from experimental data giving an
average correlation coefficient of 0.91. Batch tests were also performed to measure
the equilibrium properties of the resin, expressed as an isotherm. A Langmuir
isotherm was measured but in the concentration (color) range used, a linear fit was
deemed acceptable. This model was used to improve the Tongaat-Hulett refinery in
Durban, South Africa. The model does however, display the shortcomings of the
ICUMSA color method on which it is based. An early breakthrough of a
component that is strongly transferred to the crystal on crystallization could easily
go unnoticed.
2.3.3 Chromatography
Sugar solutions may also be purified using chromatography. This is a
technique where a pulse of sugar solution is injected into a mobile phase that passes
through a media, typically an ion exchange resin. In a favorable case different
components in solution have differing affinities for the resin. If a pulse of material
is introduced at the top of a packed-bed, into the mobile phase, the components will
move down the columns at differing speeds causing separation. For an industrial
operation, a simulated moving bed (SMB) design is often used, as it simulates a
counter-current separation process, reducing the amount of resin required. The
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French process engineering company, Applexion, have designed a process to give a
column efficiency increase of 100% (Paananen & Rousset, 2001).
There are numerous possibilities in applying chromatographic separation
techniques to the cane sugar industry (Paillat & Cotillon, 2000; Kearney, 2002).
Desugarization of final molasses is possible providing that the feed material is free
of suspended solids. This is a significant problem for cane final molasses
desugarization as the pretreatment to remove the suspended solids is difficult
(Kearney & Kochergin, 2001). This process is more effective in the beet sugar
industry as higher final molasses purities are experienced helping the process
economics. Kearney & Kochergin (2001) report that the process economics are
marginal for cane sugar operations. One of the problems associated with sucrose
recovery from final molasses is the inhibiting effect of divalent cations, particularly
calcium. Softening is also required as a pretreatment. A similar process is
described by Lancrenon et al (1998) for the chromatographic separation of refinery
molasses.
Another option is the removal of non-sucrose products from molasses.
Glycerin and other products can be recovered from cane molasses stillage after the
production of ethanol (Kampen & Saska, 1999). Peacock (1999) showed that syrup
rich in invert sugars could be separated from final molasses. Unlike, sucrose
recovery, the above-mentioned processes were not affected by divalent cations in
laboratory and pilot scale studies. The economics of these processes is determined
by the product prices (Kearney & Kochergin, 2001).
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Chromatography of refinery syrup is also possible. Kearney (1999b)
showed that refinery syrup at 84% purity could be upgraded to 90% with 90% color
removal and 96% invert sugar removal.
Extensive testing has been performed on the chromatography of evaporator
syrup prior to crystallization in the raw sugar mill (Kearney, 1997). The syrup must
be filtered and softened (removal of calcium) prior to chromatography. The
chromatography upgrades the syrup to 98% purity and removes enough color to
allow the direct crystallization of white sugar (Kochergin et al, 2000, 2001).
2.4 Color Transfer in Crystallization
A colorant (or impurity) can be transferred to the sucrose crystal on
crystallization in three mechanisms (Godshall & Baunsgaard, 2000):
Adsorption onto the crystal surface
Co-crystallization into the crystal matrix (occlusion)
Trapped by liquid inclusions inside the crystal
Godshall & Baunsgaard (2000) focused on occlusion (co-crystallization) of
colorants into the crystal matrix. Carbohydrate-type material was found to have a
greater tendency to be occluded in the crystal. In addition, the higher the molecular
weight the greater the occlusion. As a whole, color transferred 10-20% into the
crystal, but color is not one entity, and different types of colors will have a greater
or lesser affinity for the crystal. One of the greatest problems are polysaccharides as
these species are indigenous in the cane and complex with color molecules,
pulling them into the sugar crystal as the polysaccharide material is occluded.
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Lionnet (1998) extensively studied the incorporation of impurities into the
sucrose crystal on crystallization. It was concluded that color (and other impurities)
were not transferred exclusively by liquid inclusions. Two mechanisms for transfer
were investigated: adsorption isotherms and partition coefficients. Impurities can be
adsorbed into crystals by an equilibrium process, governed by an isotherm
(Donovan & Williams, 1992; Grimsey & Herrington, 1994). Witcamp and von
Rosmalen (1990) and Zumstein et al (1990) proposed the use of a partition
coefficient to measure transfer of impurities into a crystal. The partition coefficient
method was found to be applicable to the case of sugar crystallization. The partition
coefficient of a particular species i is defined as:
{ }
{ }solution
crystal
i
i
i
C
CP = (2.2)
Ideal behavior occurs when is constant for a wide range of impurity
concentrations. Factors such as rate of crystallization, temperature and crystal size
must be kept constant. Lionnet (1998) applied the partition coefficient theory to the
case of sugar crystallization and measured an ICUMSA color transfer coefficient of
0.02 (color in crystal/color in feed liquor) to affinated sugar.
iP
The issue of color transfer on crystallization needs further discussion. In the
past color has been treated as a single component measured as ICUMSA color. By
using more advanced techniques, as discussed earlier in this chapter, color may be
split into a number of components or pseudo-components, depending on the
complexity of the analysis. Owing to differences in the characteristics of these
components, it is likely that different components will have different partition
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19
coefficients (affinities) for the sucrose crystal on crystallization. This leads to the
concept of good and bad color. Good color is color that does not transfer into
the sucrose crystal and conversely bad color is material that displays high affinity
for the sucrose crystal. Color separation processes need only focus on bad color,
as good color will ultimately leave the process in the final molasses and not the
crystal.
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CHAPTER 3. THEORY
3.1 Axially Dispersed Packed Bed Adsorption Model
This model considers a binary liquid mixture being contacted with a porous
solid adsorbent in a packed bed reactor. One of these components is selectively
adsorbed onto the spherical particles. If the physical adsorption process is assumed
to be extremely fast relative to the convection and diffusion effects, then local
equilibrium will exist close to the adsorbent beads. This equilibrium may be
represented as an adsorption isotherm.
An adsorption isotherm is an equation that relates the concentration in the
film around the resin to the concentration on the resin bead itself. There are many
different isotherms used in practice. For a liquid-solid contacting process, generally
three isotherms are used: the linear, Langmuir or Freundlich isotherm. (See Figure
3.1)
Concentration in liquid
Concentration
on
solid
Langm uir Freundlich Linear
Figure 3.1: Common liquid phase isotherms
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3.1.1 Fluid Phase
Consider a portion of the packed column (Figure 3.2) of length dz, cross-
sectional areaA, and constant porosity. Q,C(z)
q(z) z
q(z+dz) z + dz
Q,C(z+dz)
Figure 3.2: A differential slice of a packed adsorption column
Assuming that radial effects are negligible, an unsteady-state material balance on
the solute may be performed.
( )44 344 2143421
44444 344444 21
44 344 21
phasesolidinonAccumulati
phasefluidinonAccumulatiDispersionAxial
flowFluid
1 Adzt
qAdz
t
C
z
CDA
z
CDAQCQC
dzzzdzzz
+
=
+
++
Adz
(3.1)
Dividing by and taking limits, (Note: set
A
Q=0u )
( )t
q
t
C
z
CD
z
Cu
+
=
+
1
2
2
0 (3.2)
Two fluid phase concentration boundary conditions are required.
i.) ( ) 0,0 CtzC ==
ii.) (3.3)( ) 0, == tzC
The first boundary condition is a simple Dirichlet condition that controls the
feed concentration to the column. The second condition arises by imagining a
column of infinite length. Since the column is infinitely long, it also has the
capability to adsorb an infinite amount of solute insuring that no solute ever reaches
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Simplifying,
( ) ( *1 CCkt
q=
) (3.6)
An initial condition is required for this equation,
( ) 00, ==tzq (3.7)
3.2 Plug Flow Adsorption Model
3.2.1 Governing Equations
The axial dispersion term in equation 3.2 may be negligible as Carberry and
Wendel (1963) report that this is likely if the bed depth exceeds fifty particle
diameters. In the experiments performed, the ratio of column length to particle
diameter is approximately ten times this value and so plug flow is likely. The
governing equations are the same as in the previous case (3.2 & 3.6), except that the
second derivative term is ignored in the fluid phase equation.
01
=
+
+
t
q
t
C
z
Cui
(3.8)
( *1 CCkt
q=
) (3.9)
An analytical solution is available for this system (3.10) in the case of the
linear isotherm using Laplace transforms (Rice & Do, 1995 & Morley, 1988).
( )( ) ( )
=
duzt
KkI
uzt
KkeCztC
i
o
u
zk
i
i
12
1exp1,
0
0
(3.10)
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A linear isotherm will be substituted into equation 3.9, but unlike in the
classical solution (substituting for q), it will be substituted for C . Morley (1988)
reports that the measured, ICUMSA color isotherm is Langmuir but is linear under
normal column operating conditions. Langmuir and linear isotherms have also been
experienced in the adsorption of basic yellow dye from aqueous solution using
activated carbon (Lin & Liu, 2000). On substitution of a linear isotherm:
*
( )
=
pHK
qCk
t
q
1 (3.11)
Experimental results suggest that K, the equilibrium constant, is a function
of pH (this will be discussed in section 5.3.2). Since pH is a variable that varies
with time, it makes sense to substitute for C , as it does not appear in any of the
derivative terms. This has the advantage of not requiring the derivative of the pH
with respect to time. A number of authors (Chern et al, 2001; Wu et al., 1999 &
Guibal et al, 1994) have experienced pH effects on adsorption isotherms.
*
3.2.2 Similarity Transformation
The above equations may be put into a more concise form by using the
similarity transform (method of combination of variables). Defining the variable:
iu
zt= (3.12)
This is a relative time scale, the difference between real time (from the start of the
experiment) and the local fluid residence time. Making the substitution of equation
3.12 into the governing equations is known as combination of variables or the
similarity transformation and is carried out below.
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Using the chain rule:
dC
dzz
Cdt
t
Cdz
z
C
zzt
+
=
+
(3.13)
Also, from 3.12:
iu
dzdtd = (3.14)
Equating the multipliers of dzon each side of the 3.13:
zit
C
uz
C
z
C
=
1 (3.15)
Using the same approach for dt,
zz
C
t
C
=
(3.16)
Similarly,
zz
q
t
q
=
(3.17)
Substituting the variable transformations into the governing equations (3.8 and 3.11)
yields,
011
=
+
+
qCC
uz
Cu
i
i (3.18)
=
K
qCk
q
1 (3.19)
It is convenient to substitute equation 3.19 into 3.18 to remove the derivative.
=
K
qCk
z
Cui (3.20)
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3.2.3 Conversion to Dimensionless Form
Reduction to dimensionless form is performed using and , as defined
below, where is a parameter still to be defined.x
x
=
L
z= (3.21)
Making the variable transformation and substituting for the Stanton number,
iu
LkSt
= :
=
K
qCSt
C
(3.22)
=
K
qCStx
L
uq i
1 (3.23)
Equation 3.23 can be simplified by defining as,x
iu
Lx
=
1 (3.24)
Yielding
=
K
qCSt
q
(3.25)
Substituting into the definition of ,x
=
=
i
i
i
u
zt
L
u
L
u
1
1 (3.26)
The boundary and initial conditions are essentially unchanged in the transformation,
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( ) 0,0 CC ==
C ( ) 00, ==
(3.27)( ) 00, ==q
3.2.4 Plug Flow Model Summary
( )
=
pHK
qCSt
C
(3.28)
( )
=
pHK
qCSt
q
(3.29)
3.2.5 Estimation of Stanton Number
The correlation of Wilson and Geankoplis (1966) may be used to estimate
the mass transfer of liquids in packed beds. For a Reynolds number range of
0.0016-55 and a Schmidt number range of 165-70,600:
32
Re09.1
=
DJ (3.30)
where,
0Re
udp= , ( ) 3
2
i
c Scu
k=DJ , and
ABD
=Sc (3.31)
The fluid properties of an aqueous sugar solution at 20 brix at 10oC are (Bubnik et
al, 1995):
Pa.s31064.2 =
kg/m31083=
Yielding a and a .34.0Re= 30.3=DJ
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The diffusivity of colorant can be approximated using the semi-empirical
equation of Polson (1950) which is recommended for biological solutes of
molecular weight greater than 1,000:
( )
( ) 31
151040.9
A
AB
M
KTD
= (3.32)
At 10oC and assuming a molecular weight of 6,000, m
2/s. The
Schmidt number can then be calculated, . Noting that:
111064.5 =ABD
7.194,43=Sc
p
c
d
kk
= (3.33)
The Stanton number may then be calculated
091.1==
=p
c
d
kSt
This estimation of the Stanton number will be useful in confirming the estimated
Stanton number from the regression of the model.
3.3 Numerical Solution Technique
3.3.1 The Finite Element Method
In the 1950s the term finite element was coined by aeronautical engineers
that used early computers for structural analysis (Baker & Pepper, 1991). The
method is founded in the calculus of variational boundary value problems. The
finite element (FEM) technique has been used to solve complex structural (finite
element) and fluid (computational fluid dynamics CFD) problems. It is not
necessary for the engineer to understand the rich theory of variational calculus, as a
stepwise approach has been presented by Baker and Pepper (1991). This stepwise
procedure has been programmed into FEMLAB, an application that uses MATLAB
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as its basis. Systems of differential equations and their associated boundary and
initial conditions may be entered and solved over a domain that has been discretized
by a user-defined mesh. Since the theory is well developed and the software readily
available the discussion will revolve around the methods used to get FEMLAB to
solve the system defined in section 3.2.
3.3.2 Solving Using FEMLAB
The first step to a FEMLAB solution is to define the domain and geometry,
over which the governing equations are to be solved. It is clear that this is a one-
dimensional problem so a straight-line is chosen as the geometry. At first glance,
the obvious domain to use is from zero to one. The second boundary condition is at
infinity so an extended domain must be used, as a mesh point is required for each
boundary condition. For the purposes of this problem, a value of non-dimensional
distance of twenty is sufficient. The solution to this problem forms a front that
moves down the column. Care must be taken to ensure that the front never reaches
the end of the domain.
To solve the system the general partial differential equations (PDE) module
of FEMLAB is used. The general form of a time-dependent (dynamic) problem is:
Ft
uda =+
in (3.34)
The above equation is the general system of PDEs in the domain . The solution
vector of the dependent variables is u. The time derivative is preceded by the
coefficient matrix and represents the vector of partial derivatives with respect
to the independent distance variable. Any remaining terms are placed into the
vectorF.
ad
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The boundary conditions of the domain, on , are represented for the
Neumann (constant derivative) case as:
0= n on (3.35)
In the above equation nis the outward normal, and as in equation 3.34. For the
simpler Dirichlet conditions (dependent variable equal to a constant),
30
0 on (3.36)=R
is used. The expression is substituted into the vectorR. Expanding the above PDE
to the derived case yields:
=
+
2
1
2
1
2
1
22,21,
12,11,
FF
uu
tdddd
aa
aa in (3.37)
3.3.3 FEMLAB Parameters
Converting the governing PDEs (3.28 and 3.29) and associated boundary
conditions to this general form yields the parameters to enter into FEMLAB.
=
q
Cu
=
10
00ad
=
0
C
( )
( )
=
pHK
qCSt
pHK
qCSt
F (3.38)
The boundary conditions are all of the Dirichlet form:
+=
q
CCR
0 (3.39)
These expressions may be substituted into FEMLAB to generate a solution. More
details on the numerical analysis will be given in Appendix A.5.
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CHAPTER 4. MATERIALS AND METHODS
4.1 Experiments
4.1.1 Feed Preparation
The first step before any resin experimentation is to prepare the feed
material. Syrup (at 66%brix) was collected from the Cinclaire mill and stored in a
refrigerator at 35oF for use during the research. The feed was prepared by
ultrafiltration through a 0.45m membrane. The unit used was a PallSep
Vibrating Membrane Filter (See Figure 4.1a) containing polymeric membranes
(Figure 4.1b). The flowsheet is shown in Figure 4.2.
Figure 4.1 (a,b): PallSep Vibrating Membrane Filter and membrane
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Permeate
Retentate
Steam
Figure 4.2: Ultrafiltration Flowsheet
The ultrafiltration procedure is as follows:
a.) Dilute required amount of stock syrup to approximately 30% brix and
place in feed tank
b.) Heat to approximately 65oC with steam
c.) Open feed valve and start pump
d.) Set cross membrane pressure to 100psi by adjusting flow control valve
e.) Start oscillating motor and set vibration to recommended amplitude
f.) Alter motor setting throughout run to maintain constant amplitude
throughout concentration
g.) When feed runs low turn-off oscillating motor and feed pump
h.) Washout feed tank and fill with water
i.) Heat to scalding and add a small amount of bleach
j.) Start pump and motor and clean membrane for 10 to 15min
k.) Empty tank and refill with water
l.) Heat and use to rinse membrane
4.1.2 Batch Tests
Batch tests are an important part of the research as they are a simple way of
developing an isotherm for the resin. An isotherm is an equilibrium expression,
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relating the concentration of a species in solution to that on the resin. This is useful
in modeling packed-bed adsorption, as a similar equilibrium will exist. The name
isotherm arises from the fact that the expression is only applicable at the
temperature the data was collected.
To maintain constant temperature conditions a 250ml jacketed glass beaker
was used for all tests, circulating water at 10oC from a Neslab refrigerated water
bath through the jacket. A Corning magnetic stirrer plate and stirrer bar was used to
mix the resin and syrup in the beaker.
Normally an equilibrium test involves leaving a sample in contact with the
resin for approximately six hours (Morley, 1988) to ensure equilibrium is achieved.
When the resins H+ or OH
- form are released, the pH of the solution changes
significantly. As discussed in Chapter 2, significant amounts of color can form
under these conditions. The testing procedure was shortened to thirty minutes, and
samples were taken every five minutes. This enabled an equilibrium value to be
projected from the dynamic results. This experiment also yields data on the speed
of the resin; that is how long it takes the resin to achieve equilibrium. This is of
interest, as similar mass transfer speeds will be exhibited in changes in process
conditions in a column experiment.
The experiment is carried out by placing 150ml to 160ml of feed material
into the beaker and cooling it to 10oC. Different regions of the isotherm are
investigated by altering the concentration of the feed. Volumes of resin are
measured as their packed-bed volume in a measuring cylinder. Approximately 15ml
of resin (the exact value is not important at this stage) is measured, and the water
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removed by vacuum filtration using a Buchner funnel and Whatman No. 4
qualitative filter paper. The dried material is then added to the beaker and a timer
started.
Samples are taken at five-minute intervals, starting with the initial material,
using an Eppendorf
adjustable-volume pipettor. Care must be exercised when
sampling so that no resin is removed. It is advisable to turn off the stirrer 5-10
seconds before the sample time so that the resin in the top layer of liquid can settle.
After all the samples have been taken, the exact resin is volume is measured in a
measuring cylinder.
4.1.3 Void Fraction Measurement
An important parameter in all the resin experiments is the resin packed-bed
void fraction, or the resin voidage. This is simply measured by drying
approximately 5ml of resin in a vacuum oven. The dry resin is placed into a 10ml
measuring-cylinder and 5ml of water is added by pipette. The cylinder is then
plugged and inverted a number of times to ensure complete mixing of the water and
resin. Extra water may be added to wash down any beads from the cylinder walls
above the liquid level by pipette. The resin packed-bed volume, volume of water
added, and the total volume may be used to calculate the voidage.
4.1.4 Column Loading
Three resins were investigated in the column loading experiments (Table
4.1), with three runs performed on each resin at different flow rates. Jacketed
25mm OD glass columns of 600mm length were connected to a Neslab circulating
refrigerated water-bath set to 10oC. FMI piston pumps were used to control the
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liquid flow rates in and of the column. Two pumps were used on the column as it
allowed simpler control of the liquid level above the column (Figure 4.3). The
pump at the column exit was set and not adjusted during an entire run. The level of
liquid above the resin bed was controlled by setting the flow-rate of the inlet pump.
An Oakton pH meter was placed after the column to continuously monitor the
product pH.
Table 4.1: Ion-exchange resins investigated
Resin Type FormFeed
Rohm & Haas Amberlite 252 RF Strong acid cation (SAC) H+ 20%brix UF syrup
Rohm & Haas Amberlite IRA 92 RF Weak base anion (WBA) OH- Cation product
Rohm & Haas Amberlite IRA 958 Strong base anion (decolorizing) Cl- 10%brix UF syrup
Figure 4.3: Column loading apparatus
Water-
Bath
10oC
Feed
Resevoir
pH
Before the run, the column is washed with deionized water to ensure that the
bed is free of any contaminants. At the start of the experiment, the feed is switched
from water to the appropriate solution and the time noted. A 25ml sample is drawn
at intervals and the pH noted. Different feed materials are used for each resin to
simulate the WSM process. To reduce the complexity of the investigation, a single
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resin is loaded in each experiment, as it is important to have a constant feed
composition to the column of interest. Beforehand sufficient feed must be produced
by passing ultrafiltered syrup through the appropriate resins (Table 4.1). In the case
of the decolorizing resin, 10%brix UF feed was used as this is of higher color,
shortening the required length of experiment. Each sample is analyzed with GPC
and for conductivity. The ICUMSA color of a number of samples is also
determined.
4.1.5 Resin Regeneration
After a run, the column is washed with water until the product stream is free
of color. The required regenerant (Table 4.2) must be made up and 5 to 6 bed
volumes is passed though the column at a low flow-rate (typically 30ml/min). After
regeneration, the column is washed with deionized water until the product pH
reaches a stable value.
Table 4.2: Column Regeneration
Resin Regenerant Temperature
SAC 6% HCl 25oC
WBA 10% NaOH 60oC
Decol. 10% NaCl; 0.2% NaOH 60oC
The use of methanol and ethanol washes were investigated to determine if
more color could be removed from the resin thereby increasing the capacity of the
resin in subsequent runs.
4.1.6 Color Investigation
A GPC investigation was done on a number of color formation reactions, the
aim being to determine suitable pseudo-components for modeling purposes.
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Materials: Evaporator syrup was obtained from the Cinclaire mill for the
caramelization and alkaline degradation tests. Molasses was obtained from stock at
the Audubon Sugar Institute for investigation of the Maillard reactions. Cane juice
was produced by disintegrating cane with water in a stainless steel environment
using a Jeffco disintegrator.
Caramelization and Alkaline Degradation: Syrup was boiled under constant reflux
in an atmospheric laboratory still for 30 minutes. In the case of alkaline
degradation, the syrup pH was increased with sodium hydroxide to pH 8.8.
Maillard Reactions: Conditions favoring the Maillard reactions (Newell, 1979)
were used: high temperature and brix but low purity. Molasses was maintained at
75oC in a constant temperature bath for 24 hours.
The Effect of Iron on Cane Juice: Cane juice was heated at 50oC in a water bath
for one hour. The effect of iron on cane juice was investigated by placing rusty and
acid cleaned coiled wire of equal lengths into the heating tubes. Non-enzymatic
effects were investigated by autoclaving (at 110oC for 10 minutes) the juice prior to
exposure to iron and also by the addition of one part mercuric chloride to 5,000
parts juice to denature any enzymes (Meade, 1963). For each treatment, a control
experiment was performed to check the effects without any iron in contact with the
juice.
4.1.7 Color Transfer in Crystallization
A batch pilot-plant crystallizer and centrifuge were used to produce raw
sugar from ultrafiltered syrup. Syrup form the St James mill was used in place of
the normal syrup as supplies had run out. The feed syrup, sugar and final molasses
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were analyzed with GPC and ICUMSA color to measure the color transfer
experienced. The color transfer data will be useful in investigating good and
bad color. A detailed description of the crystallization equipment is given by
Saska (2002).
4.2 Sample Analysis
4.2.1 ICUMSA Color
As mentioned in Chapter 2 ICUMSA color is the sugar industry standard
color measurement. A small amount of the sample to be analyzed (approx. 10ml) is
placed in a vial and corrected to pH 70.1 using HCl and NaOH solutions (0.5N
works best). This is a difficult task for deashed samples, as they contain little or no
buffering capacity. It is useful to use some of the initial sample to correct the pH if
pH 7 is overshot.
The sample is then diluted to a light golden color and filtered through a
0.45m syringe filter. The permeate is then analyzed with a spectrophotometer set
to 420nm. The brix of the sample analyzed is then determined using a
refractometer. ICUMSA color is defined as:
( ){ }{ } { }( )mmlengthCell(g/ml)ionConcentrat
000,10420nmAbsColor420nmICUMSA
= (4.1)
The concentration term is taken from Table 8 in the SASTA Laboratory manual
relating brix to concentration. Interpolation between points can be simplified by
fitting a curve to the line. A quadratic equation was found to be suitable as the
correlation coefficient (r2) was unity.
( ) { } {Brix9978.0Brix10021.4g/100mlionConcentrat 22 += } (4.2)
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Equipment used: Spectronic Genesys 2 Spectrophotometer
Bellingham and Stanley Ltd. RFM90 Refractometer
Orion 410A pH meter
4.2.2 Conductivity
The conductivity of every column-loading sample was analyzed using a
Fischer Acumet conductivity meter. Conductivity gives an indication of the ash
content of a sample, as solutions with more inorganic dissolved solids will generally
be conductive. Samples from the cation column have very high conductivity as they
have low pHs (high H
+
ion concentration). Two probes with different cell
constants were used for solutions of different conductivity (see Table 4.3).
Table 4.3: Conductivity probes
Conductivity Cell constant
10S/cm 1mS/cm 1cm-1
>1mS/cm 10 cm-1
4.2.3 Gel Permeation Chromatography
GPC is a separation process based on molecular size. A small sample is
injected into a stream of a buffer solution that flows into a precisely controlled pore
size gel column. The gel pores are arranged in such a size distribution that some
small material is able to diffuse into the pores whereas larger molecules are
excluded. The column may be calibrated by injecting standards of precise
molecular weight into the column. If the samples to be analyzed are of the same
molecular size shape as the standards, their weights may be read off the calibration
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concentration using standards. This was not performed as this brings greater
ambiguity to the data, as the choice of standard will affect the calibration. Different
dextran standards behaved very differently in their signal response for the same
concentration owing to differences in their chemical nature. For this reason all GPC
data has been reported in terms of their measured signal as this is a measure of
concentration.
10
100
1000
10000
100000
1000000
10000000
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Retention Time (min)
MW
Figure 4.5: GPC Molecular Weight Calibration
Samples were prepared by diluting to 7-10 %brix and filtering through a
5.0m syringe filter. Godshall et al (1988) show that a 0.45m filter removes very
high molecular weight material. This was confirmed by GPC analysis. A 5.0m
membrane filter was found to be sufficient to remove insoluble material but not
remove any dissolved high molecular weight material.
4.2.4 Analysis of GPC Chromatograms
4.2.4.1 Refractive Index
Quantitative analysis of GPC refractive index (RI) chromatograms of a
distribution of a single species is a simple numerical integration task (Figure 4.6a).
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The same applies for a number of non-overlapping species (Figure 4.6b). It may be
assumed that each peak will be made up of a normal or a Gaussian distribution
(Equation.4.3 Skoog et al, 1996):
( )( )
2
0
2max
tt
extx
= (4.3)
where is the maximum concentration attained, t is the retention time at the
peak and is the standard deviation of the curve (See figure 4.7a). The standard
deviation is a measure of the spread or the width of the peak.
maxx
0
2
0 1 2 3 4 5 6 7 8
t
x
Single species
0 1 2 3 4 5 6 7 8
t
x
Two Species (No deconvolution required)
xmax
t02
Figure 4.6(a,b): GPC RI chromatograms requiring no deconvolution
When peaks overlap, deconvolution is required. Numerical deconvolution
can be performed in a straightforward manner using a least-squares curve fitting
procedure (Katz et al, 1998). At any given time the overall signal is the sum of the
individual component peaks (Figure 4.8).
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0 2 4 6 8 10
t
X
12
x1 x2 X
Figure 4.8: Two Gaussian distributions deconvoluting a chromatogram
ForN components the recorded signalXis:
( ) ( ) ( ) ( )( )
=
=
+++=
N
i
tt
i
N
i
i
ex
txtxtxtX
1
max,
21
2
,0
....
(4.4)
By minimizing the sum-of-squares between the fitted and measured parameter using
a non-linear regression algorithm, the best-fit parameters can be determined.
MATLAB
6.1 Optimization Toolbox has a Sequential Quadratic Programming
routine that as applied to equation 4.4. Provided a reasonable initial guess and the
correct number of components is supplied a reasonable fit was obtained.
4.2.4.2 420nm Absorbance
The deconvolution technique used in the case of the RI chromatogram is
only suitable if the number of peaks can be determined by inspection. Using the
number of peaks as a free variable in the regression is not possible as it gives the
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44
algorithm too much freedom. By using several thousand components, one could
represent any chromatogram. In the case of the typical absorbance at 420nm
chromatogram, there are no distinct peaks and so it is not possible to determine the
number of components (Gaussian distributions) to use in the regression.
A more simple technique was used in this case. Color tests were performed
to determine the changes in concentration and color in different MW ranges
(Broadhurst & Rein, 2002). Using this data, retention times were picked at which
the absorbance was measured. These values were then tracked through the
experiments giving a color-MW profile of the processes.
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CHAPTER 5. RESULTS AND DISCUSSION
5.1 Color Formation Investigation
The results to the color formation experiments will be presented starting
from the simplest measurement technique, ICUMSA Color. This will be followed
by the more informative GPC analysis. The GPC analysis in this section (5.1) has
been performed by a slightly different technique since the method proposed in 4.2.4
relies on the results from this section (5.1.2). Peak-split points were chosen and the
area between them integrated. Figure 4.5 has been used to convert these points into
molecular weight (MW) ranges.
5.1.1 Caramelization and Alkaline Degradation
Simple ICUMSA Color measurement shows a threefold increase in color for
alkaline degradation, considerably more than for caramelization owing to the harsh
reaction conditions (See Figure 5.1).
0
5000
10000
15000
20000
25000
30000
Syrup Caramel ADP
ICU
MCSAColorUnits(IU)
Figure 5.1: ICUMSA Color of Caramelization and Alkaline Degradation
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GPC is a more insightful analysis into the formation of sugar colorants. The
resulting refractive index (RI) chromatograms are overlaid in Figure 5.2(a). Figure
5.2(b) shows the region of interest. Since sucrose overloads the detector, that peak
may be ignored.
0 5.00 10.00 15.00 20.00 25.00 30.00
Retention Time (min)
2-2.00x10
0
22.00x10
24.00x10
26.00x10
28.00x10
31.00x10
31.20x10
31.40x10
RIResponse(mV)
ADP
Caramel
Syrup
SugarPeak
Figure 5.2(a): RI GPC chromatograms for Caramelization and Alkaline
Degradation
8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00
Retention Time (min)
-40
-20
0
20
40
60
80
100
120
RIResponse(mV)
ADP
Caramel
Syrup
Figure 5.2(b): Region of interest in GPC chromatograms
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0
100
200
300
400
500
600
700
>2,600k 2,600k - 300k 300k - 32k 32k - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
Molecular Weight Range
RIarearesponse
Syrup Caramel ADP
Figure 5.3: Caramelization and Alkaline Degradation RI Areas
A number of peaks may be identified from the chromatograms, as indicated
on the chromatogram. By comparing these molecular weight ranges with the initial
syrup, the concentration effects of caramel and alkaline degradation product (ADP)
mechanisms as a function of molecular weight may be determined. The integrated
results are displayed as a bar chart in Figure 5.3. Increases in concentration are
noticeable in all ranges showing that sugar range material (
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0
2000
4000
6000
8000
10000
12000
14000
16000
>2,600k 2,600k - 300k 300k - 32k 32k - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
Absorbance(420nm)a
rearesponse
Syrup Caramel ADP
Figure 5.4: Caramelization and Alkaline Degradation - Absorbance at 420nm
Response
HPLC analysis of the samples was performed, analyzing the organic acid
concentrations. The difference between caramelization and alkaline degradation is
strikingly different (Table 5.1). Alkaline degradation causes the formation of
organic acids. In the thirty-minute period every acid except for aconitic acid,
approximately doubled its concentration.
Table 5.1: Organic acid concentrations (ppm) in caramel and ADP
formation
Sample Acetic Aconitic Citric Formic Lactic Malic Oxalic Propionic
Syrup 1040 2999 310 220 1418 413 33 43
Caramel 687 1141 186 153 937 234 19 n/d
ADP 2058 3243 365 437 2392 492 108 82
n/d non-detected
5.1.2 Maillard Reactions
A similar analysis was performed simulating the Maillard reactions. Figure
5.5 shows the significant increase in ICUMSA color. It is interesting to note that
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the same GPC molecular weight ranges were obtained for the Maillard reactions as
for ADP and caramelization, except that the highest range had to be extended.
Substantial increases in concentration are seen in all ranges (Figure 5.6).
0
20000
40000
60000
80000
100000
120000
140000
160000
Molasses Maillard
Figure 5.5: Increase in ICUMSA Color from the Maillard Reactions
0
500
1000
1500
2000
2500
3000
3500
>5,000k 5,000k - 300k 300k-32k 32k - 8,000 8,000 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
RIDetector-Arearesponse
Molasses Mail lard
Figure 5.6: Maillard Reactions RI Areas
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Figure 5.7 shows how the high molecular weight ranges contain insignificant
amounts of color in this reaction compared to the ranges, 32kMW and below. It is
interesting to compare the ICUMSA color data with GPC data. A greater increase
in the absorbances (Figure 5.7) is seen compared to the ICUMSA color results
(Figure 5.5). This is a result is caused by ICUMSA color being an intensity
parameter: the color per unit dissolved solid. Taking the increase in the RI areas
(Figure 5.6) into account shows the ICUMSA data to be reasonable.
0
20000
40000
60000
80000
100000
120000
>5,000k 5,000k - 300k 300k-32k 32k - 8,000 8,000 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
Absorbance(420nm)-Arearesponse
Molasses Maillard
Figure 5.7: Maillard Reactions - Absorbance area at 420nm Response
5.1.3 Cane Juice and Iron
It is well established that enzymes play an important role in the formation of
color (Coombs & Baldry, 1978). Before these enzymes are denatured by thermal
conditions in the process, they can form significant amounts of color. Iron is also
implicated in the mechanisms of color formation. Godshall (2000) reports that the
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ferrous iron (Fe2+
) can form complexes with phenolics and caramels to form darker
products. To investigate these effects three experiments were performed.
i. Untreated cane juice was exposed to iron enzymes still active
ii. Cane juice was autoclaved before exposure to iron thermally
sterilized
iii. Cane juice treated with Mercuric chloride (HgCl2) enzymes
chemically denatured
Untreated cane juice shows small but significant increases in color when
heated (Figure 5.9). The samples exposed to iron show a similar behavior (add or
subtract 5 units) except in the 7,500 to 4,000MW range where a large jump in color
is seen relative to the initial juice and the control experiment. The changes in
concentration are however too small to be significant (Figure 5.8). For the
remainder of this analysis the RI changes will not be included.
0
5
10
15
20
25
30
35
40
45
50
>300k 300k - 32k 32k - 9,500 9,500 - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
RIDetector-Arearesponse
Juice Control Clean Fe Rusty Fe
Figure 5.8: The effect of iron on untreated cane juice RI Area Response
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0
50
100
150
200
250
>300k 300k - 32k 32k - 9,500 9,500 - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
ABS(420nm)-Arearesponse
Juice Control Clean Fe Rusty Fe
Figure 5.9: The effect of iron on untreated cane juice ABS (420nm) AreaResponse
0
50
100
150
200
250
>300k 300k - 32k 32k - 9,500 9,500 - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
ABS
(420nm)-Arearesponse
Juice Control Clean Fe Rusty Fe
Figure 5.10: The effect of iron on autoclaved cane juice ABS (420nm) Area
Response
Autoclaved juice that is exposed to iron also shows the increase in color in the
7,500-4,000 MW range (Figure 5.10). The other ranges show either no change or a
slight decrease in color. The data shows that the color increase 4,000 to 2,000 MW
range is enzymatic as an increase is viewed for untreated juice but not for the tests
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when the enzymes were denatured prior to exposure. The control experiment shows
only a small change in this range and so the effect seen is the action of iron.
This suggests that color formation in the presence of iron leads to a colorant
of a specific molecular weight and that enzymes form relatively small amounts of
colorant in ranges. To confirm this conclusion a second test was performed. If after
denaturing the enzymes with mercuric chloride, cane juice produces colorant in the
7,500 to 4,000MW range, this must be due to the formation of colorant by the action
of iron.
The addition of mercuric chloride showed a very similar effect (Figure 5.11).
The only major increase in color is observed in the same range, confirming our
conclusion. No conclusive evidence can be obtained by comparing the effects of
rusty and clean iron.
0
50
100
150
200
250
>300k 300k - 32k 32k - 9,500 9,500 - 7,500 7,500 - 4,000 4,000 - 2,000 2,000 - 1,200 1,200 - 650
MW Range
ABS(420nm)-Arearesponse
Juice Control Clean Fe Rusty Fe
Figure 5.11: The effect of iron on cane juice with 1:5000 parts Mercuric
Chloride ABS (420nm) Area Response
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prior to ion exchange, not only is the resin protected from fouling but some of the
color that is likely to transfer to the crystal (bad color) is removed.
0
20
40
60
80
100
120
140
160
180
200
0 5 10 15 20 25
Retention time (min)
RISignal
Feed Syrup Permeate
Figure 5.12: Effect of ultrafiltration: GPC-RI
0
50
100
150
200
250
300
350
400
0 5 10 15 20 25
Retention time (min)
ABS420nmS
ignal
Feed Syrup Permeate
Figure 5.13: Effect of ultrafiltration: GPC-ABS 420nm
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5.3 Strong-Acid Cation Resin
5.3.1 SAC Batch Tests
The batch tests are particularly useful in analyzing the equilibrium properties
of the resin. For the cation resin, the calculated adsorption parameter increased as
the resin reached equilibrium. The most significant result of the batch testing is that
the resulting isotherms were linear (See Appendix B.1 & Figure 5.14).
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 50 100 150 200 250
C*(t)
q(t)
A B C D E F
Linear (A) Linear (B) Linear (C) Linear (D) Linear (E) Linear (F)
Figure 5.14: SAC Isotherms after 30 minutes
Linear isotherms are simple to work with and indicate that the solute, in this
case the colorant is dilute (Seader & Henley, 1998). The modeling technique using
pseudo-components depends on the assumption that the color components are dilute
so that multi-component isotherms and mass transfer relations are not required.
From the adsorption equilibrium parameter versus time (based here on the initial
concentration), , the final equilibrium value may be calculated (see Appendix
C.4, Equation 5.1). This relationship is plotted in Figure 5.15.
( )tKC0
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( ) ( )teq eKtK = 1 (5.1)
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35
Time (min)
KC0(t)
A B C D E F
A (calc) B (calc) C (calc) D (calc) E (calc) F (calc)
Figure 5.15: SAC equilibrium parameter (based on C0) versus time (10oC)
Table 5.3 displays the equilibrium parameters obtained from Figure 5.14 as
Figure 5.15 shows that after 30 minutes equilibrium has been reached. Higher
adsorption parameters are measured for the higher molecular weight components.
This means that the resin has a higher affinity for the larger colorants and will be
more effective at removing them than the low MW material.
Table 5.3: SAC isotherm parameters
Component A B C D E F
Keq 56.11 67.67 31.62 22.00 17.38 18.05
The refractive index detector can give information about what happens to the
non-colored high molecular weight material when it is contacted with the resin. The
RI deconvolution technique was used on the SAC isotherm GPC data. One peak in
particular (named Peak 5 in the deconvolution) was affected by the resin. The GPC
retention time decreased from its starting value of 18.8 to 20.55 minutes (see Figure
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5.16), showing a decrease in molecular weight from 2,000 to 900. The low pH
conditions are splitting the initial material into lower molecular weight species.
18.6
18.8
19
19.2
19.4
19.6
19.8
20
20.2
20.4
20.6
20.8
0 5 10 15 20 25 30 35
Time (min)
GPCRetentiontime(min)
Figure 5.16: Peak 5 retention time variation in SAC batch tests
5.3.2 SAC Column Tests
A typical breakthrough curve for the cation column is displayed in Figure
5.17. On the horizontal axis is plotted the relative time scale variable, , (defined
in equation 3.26) and on the vertical axis, the color concentration (measured
response from detector). The pH and conductivity are also plotted.
The product from the column is of low pH and high conductivity up until
. During this period hydrogen ions ( ) attached to the resin exchange for
cations ( etc.) in the syrup feed, lowering the pH (see
equation 5.2).
30= +H
++++ 22
Mg&Ca,K,Na
+= HpH 10log (5.2)
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0
20
40
60
80
100
120
140
160
180
0 10 20 30 40 50 60 70 80 90 100
C
0
2
4
6
8
10
12
14
16
Conductivity(mS/c
m)orpH
D D (feed) pH Conductivity
Figure 5.17: A typical SAC breakthrough curve (SAC6-D)
Conductivity is closely related to the pH as the more ions in the solution, the
higher the conductivity. As the resins supply of hydrogen ions is exhausted, the
conductivity begins to drop. It is interesting that at the conductivity drops
below the feed conductivity and the increases again. This may be caused by a
softening effect, as divalent cations in solution can exchange with monovalent
cations on the resin. The resin shows some affinity for the colored species in
solution (in this example, pseudo-component D). The colorant increased
continuously up until , where it reaches the feed value. After this point a
curious effect occurs, the product from the column increases above the feed
concentration for approximately 20 time units. This effect was found in all
experiments for the lower MW species (components D,E and F).
46=
35=
In the governing equations, (equations 3.28 and 3.29) there are two
parameters that govern the dynamics of the system, namely, the Stanton number and
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the adsorption equilibrium constant. If a constant linear isotherm is used, then the
slope of the breakthrough curve will be constantly decreasing owing to the driving
force term,
K
qCSt , tending to zero. This is shown graphically in Figure 5.18.
The mass transfer conditions in the bed therefore cannot force the concentration to
go above the feed value even if the Stanton number is pH dependent. A change in
Stanton number would result in a change of slope.
0 20 40 60 80 100 1200
20
40
60
80
100
120
140
160
180
200
C
St = 1; K = 18; C0= 180
C
C0
Figure 5.18: Constant linear isotherm model solution
If the resins affinity for the solute species (the colorant) were somehow
decreased during the run it would drastically alter the dynamics. Going back to the
linear isotherm, if decreases, then is forced to decrease, releasing material
already absorbed to the resin. This effect appears to explain the phenomena
K q
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occurring in Figure 5.17. In addition, it is interesting to note that the effect appears
to occur in parallel to the change in the pH and conductivity of the product.
Changing the pH of a colorant solution drastically affects it color, indicating
a pH sensitivity of the colorant molecule. It appears, in this case, that either or both
the resin and the colorant display a change in affinity for each other as the pH
increases. Essentially the equilibrium constant becomes a function of pH (as
mentioned in section 3.2.1). It will be assumed that this dependence will be similar
to the Arrhenius equation (5.2) that applies to the dependency most rate constants on
temperature (Fogler, 1999).
( ) RTE
r ekTk
= 0 (5.3)
Since the pH is defined as a logarithmic function, this equation will be
adapted slightly so that is high at low pH conditions and decreases
exponentially to a constant value at low pH conditions (Figure 5.19; Equation 5.4).
(pHK )
( ) 10 KeKpHK pH
+=
(5.4)
0
2
4
6
8
10
12
14
0 1 2 3 4 5 6 7
pH
K(pH)
Figure 5.19: Proposed functionality of K with pH
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Applying this to the model and solving, using some typical pH values, yields
a breakthrough (Figure 5.20) with very similar profile to that displayed in Figure
5.17. The model is not perfect, as it does not result in a breakthrough curve as linear
as the measured data but it is a lot more accurate than the constant isotherm case.
Possible causes for this are:
Expression for is not perfect(pHK )
Similar mass transfer effects i.e. ( )pHSt
0 10 20 30 40 50 60 70 80 90 100 1100
20
40
60
80
100
120
140
160
180
200
220
C
St = 1; K0= 18; K
1= 7; = 1
C
C0
Figure 5.20: Linear isotherm with K a function of pH model solution
Parameters may then be regressed us